Overview

Dataset statistics

Number of variables12
Number of observations7255
Missing cells29
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory680.3 KiB
Average record size in memory96.0 B

Variable types

Numeric10
Text2

Alerts

Calories is highly overall correlated with Carbs and 3 other fieldsHigh correlation
Carbs is highly overall correlated with Calories and 3 other fieldsHigh correlation
Fat is highly overall correlated with Calories and 2 other fieldsHigh correlation
Fiber is highly overall correlated with Carbs and 1 other fieldsHigh correlation
Fiber_Per_Gram is highly overall correlated with Carbs and 1 other fieldsHigh correlation
Protein is highly overall correlated with Calories and 4 other fieldsHigh correlation
Protein_Per_Calorie is highly overall correlated with Carbs and 2 other fieldsHigh correlation
Sat.Fat is highly overall correlated with Calories and 2 other fieldsHigh correlation
Unnamed: 0 is highly overall correlated with Protein and 1 other fieldsHigh correlation
Grams is highly skewed (γ1 = 30.13655217)Skewed
Fiber is highly skewed (γ1 = 29.42332236)Skewed
Unnamed: 0 has unique valuesUnique
Protein has 222 (3.1%) zerosZeros
Fat has 245 (3.4%) zerosZeros
Sat.Fat has 377 (5.2%) zerosZeros
Fiber has 1726 (23.8%) zerosZeros
Carbs has 474 (6.5%) zerosZeros
Protein_Per_Calorie has 194 (2.7%) zerosZeros
Fiber_Per_Gram has 1726 (23.8%) zerosZeros

Reproduction

Analysis started2023-12-10 10:02:01.346842
Analysis finished2023-12-10 10:02:31.145661
Duration29.8 seconds
Software versionydata-profiling vv4.6.3
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct7255
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3788.1184
Minimum0
Maximum7417
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size56.8 KiB
2023-12-10T15:32:31.331398image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile525.7
Q11976.5
median3790
Q35603.5
95-th percentile7054.3
Maximum7417
Range7417
Interquartile range (IQR)3627

Descriptive statistics

Standard deviation2097.7533
Coefficient of variation (CV)0.55377185
Kurtosis-1.1923858
Mean3788.1184
Median Absolute Deviation (MAD)1814
Skewness-0.0054352377
Sum27482799
Variance4400569.1
MonotonicityStrictly increasing
2023-12-10T15:32:31.645607image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
4982 1
 
< 0.1%
5008 1
 
< 0.1%
5007 1
 
< 0.1%
5006 1
 
< 0.1%
5005 1
 
< 0.1%
5004 1
 
< 0.1%
5003 1
 
< 0.1%
5002 1
 
< 0.1%
5001 1
 
< 0.1%
Other values (7245) 7245
99.9%
ValueCountFrequency (%)
0 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
12 1
< 0.1%
ValueCountFrequency (%)
7417 1
< 0.1%
7416 1
< 0.1%
7415 1
< 0.1%
7414 1
< 0.1%
7413 1
< 0.1%
7412 1
< 0.1%
7411 1
< 0.1%
7410 1
< 0.1%
7409 1
< 0.1%
7408 1
< 0.1%

Food
Text

Distinct7241
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
2023-12-10T15:32:32.351126image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length184
Median length110
Mean length39.240799
Min length3

Characters and Unicode

Total characters284692
Distinct characters73
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7228 ?
Unique (%)99.6%

Sample

1st rowCows' milk
2nd rowButtermilk
3rd rowEvaporated, undiluted
4th rowFortified milk
5th rowPowdered milk
ValueCountFrequency (%)
with 2241
 
5.0%
or 1539
 
3.5%
and 1221
 
2.7%
fat 1055
 
2.4%
added 631
 
1.4%
sauce 619
 
1.4%
cooked 592
 
1.3%
as 587
 
1.3%
to 577
 
1.3%
ns 561
 
1.3%
Other values (1843) 34861
78.4%
2023-12-10T15:32:33.231465image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
37245
 
13.1%
e 28653
 
10.1%
a 22273
 
7.8%
o 17517
 
6.2%
t 17011
 
6.0%
r 16121
 
5.7%
d 13192
 
4.6%
i 13131
 
4.6%
n 12417
 
4.4%
, 12397
 
4.4%
Other values (63) 94735
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 220686
77.5%
Space Separator 37245
 
13.1%
Other Punctuation 13101
 
4.6%
Uppercase Letter 11397
 
4.0%
Dash Punctuation 1134
 
0.4%
Close Punctuation 446
 
0.2%
Open Punctuation 446
 
0.2%
Decimal Number 237
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 28653
13.0%
a 22273
 
10.1%
o 17517
 
7.9%
t 17011
 
7.7%
r 16121
 
7.3%
d 13192
 
6.0%
i 13131
 
6.0%
n 12417
 
5.6%
s 10852
 
4.9%
c 9433
 
4.3%
Other values (16) 60086
27.2%
Uppercase Letter
ValueCountFrequency (%)
C 1909
16.8%
S 1727
15.2%
P 1250
11.0%
N 969
8.5%
B 763
 
6.7%
F 737
 
6.5%
M 496
 
4.4%
R 481
 
4.2%
T 449
 
3.9%
G 386
 
3.4%
Other values (16) 2230
19.6%
Other Punctuation
ValueCountFrequency (%)
, 12397
94.6%
/ 329
 
2.5%
; 186
 
1.4%
' 74
 
0.6%
% 67
 
0.5%
" 25
 
0.2%
. 16
 
0.1%
& 6
 
< 0.1%
: 1
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
0 109
46.0%
1 74
31.2%
2 32
 
13.5%
5 9
 
3.8%
4 6
 
2.5%
3 4
 
1.7%
9 2
 
0.8%
7 1
 
0.4%
Space Separator
ValueCountFrequency (%)
37245
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1134
100.0%
Close Punctuation
ValueCountFrequency (%)
) 446
100.0%
Open Punctuation
ValueCountFrequency (%)
( 446
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 232083
81.5%
Common 52609
 
18.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 28653
12.3%
a 22273
 
9.6%
o 17517
 
7.5%
t 17011
 
7.3%
r 16121
 
6.9%
d 13192
 
5.7%
i 13131
 
5.7%
n 12417
 
5.4%
s 10852
 
4.7%
c 9433
 
4.1%
Other values (42) 71483
30.8%
Common
ValueCountFrequency (%)
37245
70.8%
, 12397
 
23.6%
- 1134
 
2.2%
) 446
 
0.8%
( 446
 
0.8%
/ 329
 
0.6%
; 186
 
0.4%
0 109
 
0.2%
' 74
 
0.1%
1 74
 
0.1%
Other values (11) 169
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 284692
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
37245
 
13.1%
e 28653
 
10.1%
a 22273
 
7.8%
o 17517
 
6.2%
t 17011
 
6.0%
r 16121
 
5.7%
d 13192
 
4.6%
i 13131
 
4.6%
n 12417
 
4.4%
, 12397
 
4.4%
Other values (63) 94735
33.3%

Grams
Real number (ℝ)

SKEWED 

Distinct65
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.20028
Minimum12
Maximum1419
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.8 KiB
2023-12-10T15:32:33.565681image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile100
Q1100
median100
Q3100
95-th percentile100
Maximum1419
Range1407
Interquartile range (IQR)0

Descriptive statistics

Standard deviation26.067842
Coefficient of variation (CV)0.25758667
Kurtosis1242.3385
Mean101.20028
Median Absolute Deviation (MAD)0
Skewness30.136552
Sum734208
Variance679.5324
MonotonicityNot monotonic
2023-12-10T15:32:33.834633image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 7099
97.8%
85 23
 
0.3%
250 11
 
0.2%
50 10
 
0.1%
200 6
 
0.1%
346 6
 
0.1%
110 5
 
0.1%
40 4
 
0.1%
120 4
 
0.1%
70 4
 
0.1%
Other values (55) 83
 
1.1%
ValueCountFrequency (%)
12 1
 
< 0.1%
14 3
 
< 0.1%
16 1
 
< 0.1%
20 3
 
< 0.1%
23 3
 
< 0.1%
28 3
 
< 0.1%
30 3
 
< 0.1%
40 4
 
0.1%
50 10
0.1%
52 1
 
< 0.1%
ValueCountFrequency (%)
1419 1
 
< 0.1%
976 1
 
< 0.1%
925 1
 
< 0.1%
540 1
 
< 0.1%
454 2
 
< 0.1%
346 6
0.1%
300 1
 
< 0.1%
270 1
 
< 0.1%
260 1
 
< 0.1%
257 1
 
< 0.1%

Calories
Real number (ℝ)

HIGH CORRELATION 

Distinct5413
Distinct (%)74.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200.46245
Minimum0
Maximum3969
Zeros28
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size56.8 KiB
2023-12-10T15:32:34.155185image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile31.405
Q183.35
median165.63
Q3276.64
95-th percentile476.41
Maximum3969
Range3969
Interquartile range (IQR)193.29

Descriptive statistics

Standard deviation155.48684
Coefficient of variation (CV)0.77564074
Kurtosis53.429484
Mean200.46245
Median Absolute Deviation (MAD)93.67
Skewness3.4063122
Sum1454355
Variance24176.157
MonotonicityNot monotonic
2023-12-10T15:32:34.495667image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 28
 
0.4%
65.33 20
 
0.3%
66.53 16
 
0.2%
65.54 16
 
0.2%
900 16
 
0.2%
66.41 15
 
0.2%
205.26 13
 
0.2%
65.15 12
 
0.2%
177.67 11
 
0.2%
66.04 11
 
0.2%
Other values (5403) 7097
97.8%
ValueCountFrequency (%)
0 28
0.4%
0.32 1
 
< 0.1%
0.4 9
 
0.1%
0.48 1
 
< 0.1%
0.52 1
 
< 0.1%
0.53 1
 
< 0.1%
0.54 1
 
< 0.1%
0.56 1
 
< 0.1%
0.6 1
 
< 0.1%
0.66 5
 
0.1%
ValueCountFrequency (%)
3969 1
 
< 0.1%
1963 2
 
< 0.1%
1210 1
 
< 0.1%
1207 1
 
< 0.1%
1182 1
 
< 0.1%
990 1
 
< 0.1%
900.26 1
 
< 0.1%
900 16
0.2%
899.73 1
 
< 0.1%
896.44 1
 
< 0.1%

Protein
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2116
Distinct (%)29.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.7129318
Minimum-1
Maximum232
Zeros222
Zeros (%)3.1%
Negative1
Negative (%)< 0.1%
Memory size56.8 KiB
2023-12-10T15:32:34.828221image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0.16
Q12.22
median6.2
Q312.255
95-th percentile25.243
Maximum232
Range233
Interquartile range (IQR)10.035

Descriptive statistics

Standard deviation8.9558154
Coefficient of variation (CV)1.0278762
Kurtosis62.180572
Mean8.7129318
Median Absolute Deviation (MAD)4.52
Skewness3.9870392
Sum63212.32
Variance80.20663
MonotonicityNot monotonic
2023-12-10T15:32:35.062555image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 222
 
3.1%
0.1 35
 
0.5%
2 34
 
0.5%
3 30
 
0.4%
1.4 29
 
0.4%
0.3 29
 
0.4%
0.2 29
 
0.4%
1.6 23
 
0.3%
1.38 23
 
0.3%
4 23
 
0.3%
Other values (2106) 6778
93.4%
ValueCountFrequency (%)
-1 1
 
< 0.1%
0 222
3.1%
0.01 3
 
< 0.1%
0.02 5
 
0.1%
0.03 5
 
0.1%
0.04 8
 
0.1%
0.05 4
 
0.1%
0.06 4
 
0.1%
0.07 5
 
0.1%
0.08 14
 
0.2%
ValueCountFrequency (%)
232 1
< 0.1%
114 2
< 0.1%
89 1
< 0.1%
78.13 2
< 0.1%
76.25 1
< 0.1%
66.67 1
< 0.1%
64.06 1
< 0.1%
62.82 2
< 0.1%
61.3 1
< 0.1%
58.94 1
< 0.1%

Fat
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2112
Distinct (%)29.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.0930172
Minimum0
Maximum233
Zeros245
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size56.8 KiB
2023-12-10T15:32:35.266276image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.07
Q12.06
median5.55
Q312.71
95-th percentile27.993
Maximum233
Range233
Interquartile range (IQR)10.65

Descriptive statistics

Standard deviation11.606483
Coefficient of variation (CV)1.2764172
Kurtosis36.526698
Mean9.0930172
Median Absolute Deviation (MAD)4.61
Skewness4.2214346
Sum65969.84
Variance134.71045
MonotonicityNot monotonic
2023-12-10T15:32:35.456950image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 245
 
3.4%
0.1 77
 
1.1%
0.2 51
 
0.7%
0.3 32
 
0.4%
0.08 31
 
0.4%
3.49 31
 
0.4%
0.02 29
 
0.4%
1 29
 
0.4%
0.07 26
 
0.4%
3.41 25
 
0.3%
Other values (2102) 6679
92.1%
ValueCountFrequency (%)
0 245
3.4%
0.01 18
 
0.2%
0.02 29
 
0.4%
0.03 13
 
0.2%
0.04 14
 
0.2%
0.05 11
 
0.2%
0.06 11
 
0.2%
0.07 26
 
0.4%
0.08 31
 
0.4%
0.09 15
 
0.2%
ValueCountFrequency (%)
233 1
 
< 0.1%
115 2
 
< 0.1%
110 1
 
< 0.1%
100 16
0.2%
99.98 1
 
< 0.1%
99.97 1
 
< 0.1%
99.48 1
 
< 0.1%
99.1 1
 
< 0.1%
92.18 1
 
< 0.1%
87.34 1
 
< 0.1%

Sat.Fat
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3504
Distinct (%)48.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2.98776
Minimum0
Maximum234
Zeros377
Zeros (%)5.2%
Negative0
Negative (%)0.0%
Memory size56.8 KiB
2023-12-10T15:32:35.668646image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.5
median1.465
Q33.7575
95-th percentile10.13025
Maximum234
Range234
Interquartile range (IQR)3.2575

Descriptive statistics

Standard deviation5.6245185
Coefficient of variation (CV)1.8825202
Kurtosis466.52729
Mean2.98776
Median Absolute Deviation (MAD)1.3045
Skewness14.974845
Sum21673.211
Variance31.635208
MonotonicityNot monotonic
2023-12-10T15:32:35.960153image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 377
 
5.2%
0.002 40
 
0.6%
0.4 19
 
0.3%
0.014 18
 
0.2%
1 18
 
0.2%
0.009 18
 
0.2%
1.429 17
 
0.2%
0.02 17
 
0.2%
0.018 17
 
0.2%
1.24 16
 
0.2%
Other values (3494) 6697
92.3%
ValueCountFrequency (%)
0 377
5.2%
0.001 9
 
0.1%
0.002 40
 
0.6%
0.003 10
 
0.1%
0.004 8
 
0.1%
0.005 9
 
0.1%
0.006 12
 
0.2%
0.007 5
 
0.1%
0.008 14
 
0.2%
0.009 18
 
0.2%
ValueCountFrequency (%)
234 1
 
< 0.1%
116 2
< 0.1%
92 1
 
< 0.1%
88 1
 
< 0.1%
82.5 1
 
< 0.1%
61.924 1
 
< 0.1%
51.368 3
< 0.1%
46.24 1
 
< 0.1%
46.235 1
 
< 0.1%
45.39 3
< 0.1%

Fiber
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct164
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7558112
Minimum0
Maximum235
Zeros1726
Zeros (%)23.8%
Negative0
Negative (%)0.0%
Memory size56.8 KiB
2023-12-10T15:32:36.317203image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1
median1
Q32.1
95-th percentile6.3
Maximum235
Range235
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.269807
Coefficient of variation (CV)2.4318145
Kurtosis1385.9865
Mean1.7558112
Median Absolute Deviation (MAD)1
Skewness29.423322
Sum12738.41
Variance18.231252
MonotonicityNot monotonic
2023-12-10T15:32:36.650668image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1726
23.8%
0.6 313
 
4.3%
1.2 214
 
2.9%
1 207
 
2.9%
0.2 204
 
2.8%
0.9 203
 
2.8%
0.4 201
 
2.8%
1.1 196
 
2.7%
0.7 190
 
2.6%
0.3 188
 
2.6%
Other values (154) 3613
49.8%
ValueCountFrequency (%)
0 1726
23.8%
0.1 175
 
2.4%
0.2 204
 
2.8%
0.3 188
 
2.6%
0.31 1
 
< 0.1%
0.4 201
 
2.8%
0.5 138
 
1.9%
0.6 313
 
4.3%
0.7 190
 
2.6%
0.8 184
 
2.5%
ValueCountFrequency (%)
235 1
< 0.1%
117 2
< 0.1%
67.5 1
< 0.1%
46.2 1
< 0.1%
42.8 1
< 0.1%
37.5 1
< 0.1%
37 1
< 0.1%
29.3 1
< 0.1%
27.3 2
< 0.1%
26.9 1
< 0.1%

Carbs
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3137
Distinct (%)43.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.943391
Minimum0
Maximum236
Zeros474
Zeros (%)6.5%
Negative0
Negative (%)0.0%
Memory size56.8 KiB
2023-12-10T15:32:37.233723image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.565
median13.29
Q326.405
95-th percentile71.64
Maximum236
Range236
Interquartile range (IQR)20.84

Descriptive statistics

Standard deviation22.468601
Coefficient of variation (CV)1.0728254
Kurtosis4.7151198
Mean20.943391
Median Absolute Deviation (MAD)9.15
Skewness1.7861498
Sum151944.3
Variance504.83802
MonotonicityNot monotonic
2023-12-10T15:32:37.561784image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 474
 
6.5%
0.1 88
 
1.2%
12.51 36
 
0.5%
13.1 30
 
0.4%
9.85 30
 
0.4%
6.87 23
 
0.3%
9.86 16
 
0.2%
1 15
 
0.2%
7.45 15
 
0.2%
0.2 14
 
0.2%
Other values (3127) 6514
89.8%
ValueCountFrequency (%)
0 474
6.5%
0.01 3
 
< 0.1%
0.03 2
 
< 0.1%
0.04 3
 
< 0.1%
0.05 2
 
< 0.1%
0.06 9
 
0.1%
0.07 5
 
0.1%
0.08 3
 
< 0.1%
0.09 12
 
0.2%
0.1 88
 
1.2%
ValueCountFrequency (%)
236 1
< 0.1%
229 1
< 0.1%
216 1
< 0.1%
199 1
< 0.1%
154 1
< 0.1%
119 1
< 0.1%
118 2
< 0.1%
100 2
< 0.1%
99.77 1
< 0.1%
99.6 2
< 0.1%
Distinct2445
Distinct (%)33.7%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
2023-12-10T15:32:38.196260image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length79
Median length62
Mean length14.102274
Min length3

Characters and Unicode

Total characters102312
Distinct characters71
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1754 ?
Unique (%)24.2%

Sample

1st rowDairy products
2nd rowDairy products
3rd rowDairy products
4th rowDairy products
5th rowDairy products
ValueCountFrequency (%)
or 665
 
4.0%
with 664
 
4.0%
and 494
 
3.0%
chicken 475
 
2.9%
sandwich 297
 
1.8%
egg 285
 
1.7%
rice 271
 
1.6%
beef 265
 
1.6%
sauce 234
 
1.4%
potato 229
 
1.4%
Other values (1347) 12697
76.6%
2023-12-10T15:32:39.226411image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 10829
 
10.6%
9324
 
9.1%
a 8608
 
8.4%
r 6416
 
6.3%
o 6116
 
6.0%
t 5786
 
5.7%
i 5410
 
5.3%
s 5143
 
5.0%
n 4769
 
4.7%
c 3758
 
3.7%
Other values (61) 36153
35.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 83780
81.9%
Space Separator 9324
 
9.1%
Uppercase Letter 8355
 
8.2%
Other Punctuation 311
 
0.3%
Open Punctuation 185
 
0.2%
Close Punctuation 181
 
0.2%
Dash Punctuation 162
 
0.2%
Decimal Number 14
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 10829
12.9%
a 8608
 
10.3%
r 6416
 
7.7%
o 6116
 
7.3%
t 5786
 
6.9%
i 5410
 
6.5%
s 5143
 
6.1%
n 4769
 
5.7%
c 3758
 
4.5%
l 3693
 
4.4%
Other values (16) 23252
27.8%
Uppercase Letter
ValueCountFrequency (%)
C 1766
21.1%
P 1095
13.1%
B 753
9.0%
S 735
8.8%
F 486
 
5.8%
M 448
 
5.4%
T 411
 
4.9%
R 339
 
4.1%
G 277
 
3.3%
E 247
 
3.0%
Other values (16) 1798
21.5%
Other Punctuation
ValueCountFrequency (%)
, 180
57.9%
' 62
 
19.9%
/ 37
 
11.9%
" 22
 
7.1%
& 6
 
1.9%
% 2
 
0.6%
; 1
 
0.3%
: 1
 
0.3%
Decimal Number
ValueCountFrequency (%)
0 4
28.6%
2 3
21.4%
3 2
14.3%
5 2
14.3%
1 1
 
7.1%
9 1
 
7.1%
7 1
 
7.1%
Space Separator
ValueCountFrequency (%)
9324
100.0%
Open Punctuation
ValueCountFrequency (%)
( 185
100.0%
Close Punctuation
ValueCountFrequency (%)
) 181
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 162
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 92135
90.1%
Common 10177
 
9.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 10829
 
11.8%
a 8608
 
9.3%
r 6416
 
7.0%
o 6116
 
6.6%
t 5786
 
6.3%
i 5410
 
5.9%
s 5143
 
5.6%
n 4769
 
5.2%
c 3758
 
4.1%
l 3693
 
4.0%
Other values (42) 31607
34.3%
Common
ValueCountFrequency (%)
9324
91.6%
( 185
 
1.8%
) 181
 
1.8%
, 180
 
1.8%
- 162
 
1.6%
' 62
 
0.6%
/ 37
 
0.4%
" 22
 
0.2%
& 6
 
0.1%
0 4
 
< 0.1%
Other values (9) 14
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 102312
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 10829
 
10.6%
9324
 
9.1%
a 8608
 
8.4%
r 6416
 
6.3%
o 6116
 
6.0%
t 5786
 
5.7%
i 5410
 
5.3%
s 5143
 
5.0%
n 4769
 
4.7%
c 3758
 
3.7%
Other values (61) 36153
35.3%

Protein_Per_Calorie
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct5719
Distinct (%)79.1%
Missing28
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean5.0246922
Minimum-0.71942446
Maximum25
Zeros194
Zeros (%)2.7%
Negative1
Negative (%)< 0.1%
Memory size56.8 KiB
2023-12-10T15:32:39.576355image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-0.71942446
5-th percentile0.33519188
Q11.8409048
median3.7199125
Q36.6812924
95-th percentile14.729827
Maximum25
Range25.719424
Interquartile range (IQR)4.8403876

Descriptive statistics

Standard deviation4.5558893
Coefficient of variation (CV)0.90670017
Kurtosis2.6298591
Mean5.0246922
Median Absolute Deviation (MAD)2.1982082
Skewness1.6079353
Sum36313.45
Variance20.756127
MonotonicityNot monotonic
2023-12-10T15:32:39.891124image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 194
 
2.7%
2.090326518 16
 
0.2%
2.228580033 15
 
0.2%
25 13
 
0.2%
13.49020754 12
 
0.2%
16.45184893 11
 
0.2%
2.089642641 11
 
0.2%
2.430395324 10
 
0.1%
2.479087452 10
 
0.1%
2.479718353 10
 
0.1%
Other values (5709) 6925
95.5%
(Missing) 28
 
0.4%
ValueCountFrequency (%)
-0.71942446 1
 
< 0.1%
0 194
2.7%
0.007664401 1
 
< 0.1%
0.012218692 1
 
< 0.1%
0.021922914 2
 
< 0.1%
0.022714367 1
 
< 0.1%
0.025087807 1
 
< 0.1%
0.027754649 1
 
< 0.1%
0.02789303 1
 
< 0.1%
0.028846806 1
 
< 0.1%
ValueCountFrequency (%)
25 13
0.2%
24.00465658 1
 
< 0.1%
23.92006776 1
 
< 0.1%
23.91922361 1
 
< 0.1%
23.9127046 1
 
< 0.1%
23.84018619 1
 
< 0.1%
23.58348023 1
 
< 0.1%
23.57988166 1
 
< 0.1%
23.5640648 1
 
< 0.1%
23.52806152 1
 
< 0.1%

Fiber_Per_Gram
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct216
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.017287838
Minimum0
Maximum1.0446429
Zeros1726
Zeros (%)23.8%
Negative0
Negative (%)0.0%
Memory size56.8 KiB
2023-12-10T15:32:40.195779image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.001
median0.01
Q30.021
95-th percentile0.0623
Maximum1.0446429
Range1.0446429
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.03312618
Coefficient of variation (CV)1.9161552
Kurtosis388.69901
Mean0.017287838
Median Absolute Deviation (MAD)0.01
Skewness14.485105
Sum125.42326
Variance0.0010973438
MonotonicityNot monotonic
2023-12-10T15:32:40.563784image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1726
23.8%
0.006 313
 
4.3%
0.012 209
 
2.9%
0.002 205
 
2.8%
0.01 202
 
2.8%
0.004 201
 
2.8%
0.009 199
 
2.7%
0.011 195
 
2.7%
0.007 186
 
2.6%
0.003 185
 
2.5%
Other values (206) 3634
50.1%
ValueCountFrequency (%)
0 1726
23.8%
0.000625 1
 
< 0.1%
0.000714286 1
 
< 0.1%
0.000740741 2
 
< 0.1%
0.0008 1
 
< 0.1%
0.000833333 1
 
< 0.1%
0.000925926 2
 
< 0.1%
0.00098661 1
 
< 0.1%
0.001 164
 
2.3%
0.001333333 1
 
< 0.1%
ValueCountFrequency (%)
1.044642857 2
< 0.1%
1.02173913 1
< 0.1%
0.462 1
< 0.1%
0.428 1
< 0.1%
0.4 1
< 0.1%
0.375 1
< 0.1%
0.37 1
< 0.1%
0.293 1
< 0.1%
0.273 2
< 0.1%
0.269 1
< 0.1%

Interactions

2023-12-10T15:32:27.069561image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:04.945775image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:07.744613image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:10.463928image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:13.130796image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:15.339516image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:17.615123image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:19.880544image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:22.205744image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:24.438537image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:27.352070image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:05.246407image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:08.081558image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:10.736670image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:13.369868image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:15.555046image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:17.806371image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:20.151953image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:22.440469image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:24.706056image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:27.609308image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:05.501621image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:08.344271image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:11.001238image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:13.614263image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:16.071819image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:18.002936image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:20.409322image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:22.663283image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:25.007241image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:27.907611image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:05.794157image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:08.659851image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:11.314032image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:13.891043image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:16.280520image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:18.217794image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:20.657997image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:22.949668image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:25.314314image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:28.161852image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:06.076788image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:08.916703image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:11.559038image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:14.126911image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:16.516321image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:18.427680image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:20.880540image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:23.153191image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:25.542183image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:28.422416image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:06.401788image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:09.207557image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:11.842119image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:14.358773image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:16.730098image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:18.632957image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:21.098233image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:23.377791image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:25.802105image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:28.649989image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:06.740577image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:09.446816image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:12.091286image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:14.529603image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:16.931324image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:18.870429image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:21.276918image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:23.561774image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:26.063755image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:28.897544image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:06.969146image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:09.698836image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:12.375458image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:14.735314image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:17.127057image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:19.225786image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:21.491605image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:23.778388image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:26.320533image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:29.371103image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:07.246727image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:09.938579image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:12.623326image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:14.914480image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:17.271278image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:19.451137image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:21.717606image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:24.001129image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:26.569809image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:29.603553image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:07.490137image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:10.204837image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:12.877368image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:15.119325image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:17.465523image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:19.676847image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:21.968425image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:24.230821image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T15:32:26.805860image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-12-10T15:32:40.799172image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
CaloriesCarbsFatFiberFiber_Per_GramGramsProteinProtein_Per_CalorieSat.FatUnnamed: 0
Calories1.0000.5630.7920.3020.3020.0300.521-0.1180.697-0.195
Carbs0.5631.0000.1650.6440.6420.094-0.100-0.5520.1090.231
Fat0.7920.1651.0000.1160.117-0.0220.5530.0640.927-0.324
Fiber0.3020.6440.1161.0000.9980.030-0.048-0.2840.0230.334
Fiber_Per_Gram0.3020.6420.1170.9981.000-0.004-0.049-0.2850.0240.335
Grams0.0300.094-0.0220.030-0.0041.000-0.005-0.014-0.014-0.019
Protein0.521-0.1000.553-0.048-0.049-0.0051.0000.7340.511-0.582
Protein_Per_Calorie-0.118-0.5520.064-0.284-0.285-0.0140.7341.0000.090-0.508
Sat.Fat0.6970.1090.9270.0230.024-0.0140.5110.0901.000-0.363
Unnamed: 0-0.1950.231-0.3240.3340.335-0.019-0.582-0.508-0.3631.000

Missing values

2023-12-10T15:32:29.972092image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:32:30.561937image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-10T15:32:30.990916image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0FoodGramsCaloriesProteinFatSat.FatFiberCarbsCategoryProtein_Per_CalorieFiber_Per_Gram
00Cows' milk976680.032.040.036.00.048.0Dairy products4.7058820.000000
12Buttermilk246133.09.05.04.00.013.0Dairy products6.7669170.000000
23Evaporated, undiluted252340.016.020.018.00.024.0Dairy products4.7058820.000000
34Fortified milk14191210.089.042.023.01.4119.0Dairy products7.3553720.000987
45Powdered milk103516.027.028.024.00.039.0Dairy products5.2325580.000000
58Goats' milk244166.08.010.08.00.011.0Dairy products4.8192770.000000
69(1/2 cup ice cream)540592.024.024.022.00.070.0Dairy products4.0540540.000000
710Cocoa252235.08.011.010.00.026.0Dairy products3.4042550.000000
811skim. milk250160.018.04.03.01.013.0Dairy products11.2500000.004000
912(cornstarch)248286.09.010.09.00.040.0Dairy products3.1468530.000000
Unnamed: 0FoodGramsCaloriesProteinFatSat.FatFiberCarbsCategoryProtein_Per_CalorieFiber_Per_Gram
72457408Cauliflower, cooked, as ingredient10031.332.000.290.1352.15.18Cauliflower6.3836580.021
72467409Eggplant, cooked, as ingredient10031.191.050.190.0373.26.32Eggplant3.3664640.032
72477410Green beans, cooked, as ingredient10038.751.910.230.0522.87.26Green beans4.9290320.028
72487411Summer squash, cooked, as ingredient10024.831.310.350.1041.24.11Summer squash5.2758760.012
72497412Dark green vegetables as ingredient in omelet10037.002.970.400.0782.55.38Dark green vegetables as ingredient in omelet8.0270270.025
72507413Tomatoes as ingredient in omelet10028.431.110.230.0381.65.48Tomatoes as ingredient in omelet3.9043260.016
72517414Other vegetables as ingredient in omelet10036.503.460.380.0611.44.81Other vegetables as ingredient in omelet9.4794520.014
72527415Vegetables as ingredient in curry10055.351.810.190.0512.211.60Vegetables as ingredient in curry3.2700990.022
72537416Sauce as ingredient in hamburgers100279.571.3422.853.5440.617.14Sauce as ingredient in hamburgers0.4793080.006
72547417Industrial oil as ingredient in food100900.000.00100.0032.6720.00.00Industrial oil as ingredient in food0.0000000.000